AI-Driven Mitigation of Cognitive Biases in Intelligent Personal Assistant Interactions: Evidence from African Contexts
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96-115
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Jun 30, 2025
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10.58578/kijeit.v2i3.6480
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Authors:Abdullahi A. Shinkafi1, Steve Bassey2, Shammah Emmanuel Chaku3, Gilbert I. O. Aimufua4, Abraham D. Joseph5
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Abstract
This paper presents a rigorous investigation into how artificial intelligence-driven features in Intelligent Personal Assistants (IPAs) can mitigate cognitive biases within the culturally diverse landscapes of African societies. Positioned at the intersection of cognitive psychology, artificial intelligence, and African cultural studies, the research examines how traditional decision-making patterns in West, Southern, and Central African contexts interact with AI-powered debiasing mechanisms. Grounded in Dual-Process Theory and indigenous knowledge systems, the study explores how IPAs can be culturally calibrated to address confirmation bias, anchoring, and availability heuristics as they uniquely manifest within African socio-cultural frameworks. Employing a sequential explanatory mixed-methods design, the study integrates survey data from 528 participants across eight countries with 40 in-depth interviews. The findings reveal that while AI-driven interventions significantly reduce cognitive biases, their effectiveness is deeply moderated by cultural dimensions such as power distance, uncertainty avoidance, and collectivist orientations—each varying distinctly across regions. Culturally contextualized nudges and interventions aligned with local values and communication norms yielded the strongest debiasing outcomes. This research offers essential empirical insights into the emerging field of culturally responsive AI design, emphasizing the need to recalibrate debiasing techniques to reflect and respect African cultural perspectives rather than applying Western-centric models of cognitive optimization.
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Cognitive Bias; Artificial Intelligence; Intelligent Personal Assistants; Debiasing; Human-AI Interaction; Decision Support Systems; African Technology Users
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How to Cite
Shinkafi, A. A., Bassey, S., Chaku, S. E., Aimufua, G. I. O., & Joseph, A. D. (2025). AI-Driven Mitigation of Cognitive Biases in Intelligent Personal Assistant Interactions: Evidence from African Contexts. Kwaghe International Journal of Engineering and Information Technology, 2(3), 96-115. https://doi.org/10.58578/kijeit.v2i3.6480
References
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37. Zhang, W., Lee, S., & Kim, J. (2021). Personalized nudges and anchoring bias in East Asian e-commerce platforms. International Journal of Human-Computer Interaction, 37(15), 1420–1431.
38. Müller, H., Dubois, C., & Rossi, S. (2020). Statistical framing reduces availability heuristic in European financial decision-making. Journal of Behavioral Finance, 21(4), 380–392.
39. McSweeney, B. (2002). Hofstede’s model of national cultural differences and their consequences: A triumph of faith—A failure of analysis. Human Relations, 55(1), 89–118.
40. Baskerville, R. F. (2003). Hofstede never studied culture. Accounting, Organizations and Society, 28(1), 1–14.
41. Oppong, N. Y. (2013). Guide from Hofstede’s cultural dimensions. Cross-Cultural Management Journal, 15(1), 67–72.
42. Woldu, H. G. (2001). Looking at African value system through cultural dimensions. International Conference on African Development Archives, Paper 25.
2. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
3. Ahmed, S., & Osman, R. (2022). Cultural influences on cognitive biases in decision making: A review. Journal of Cross-Cultural Psychology, 53(2), 150–175.
4. Hofstede, G. (1980). Culture’s consequences: International differences in work-related values. Sage Publications.
5. Haselton, M. G., Nettle, D., & Andrews, P. W. (2015). The evolution of cognitive bias. In D. M. Buss (Ed.), The handbook of evolutionary psychology (pp. 724–746). John Wiley & Sons.
6. Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220.
7. Furnham, A., & Boo, H. C. (2011). A literature review of the anchoring effect. The Journal of Socio-Economics, 40(1), 35–42.
8. Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232.
9. Samson, A. (Ed.). (2020). The behavioral economics guide 2020. Behavioral Science Solutions Ltd.
10. Baker, H. K., & Puttonen, V. (Eds.). (2017). Investment traps exposed: Navigating investor mistakes and behavioral biases. Emerald Group Publishing.
11. Blumenthal-Barby, D., & Burroughs, H. S. (2012). Seeking better health care outcomes: The ethics of using the nudge. The American Journal of Bioethics, 12(2), 1–10.
12. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
13. Arnott, D. (2006). Cognitive biases and decision support systems development: A design science approach. Information Systems Journal, 16(1), 55–78.
14. Soll, J. B., Milkman, K. L., & Payne, J. W. (2015). A user’s guide to debiasing. In G. Keren & G. Wu (Eds.), The Wiley Blackwell handbook of judgment and decision making (pp. 924–951). John Wiley & Sons.
15. Bhandari, A., Neben, T., & Wendt, K. W. (2019). Beyond the hype: A critical review and research agenda for explainable artificial intelligence. Business Information Systems Engineering, 61(4), 429–444.
16. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
17. Gunning, D., et al. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4(37), eaav3229.
18. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2018). Overcoming algorithm aversion: People will use algorithms if they can (even slightly) modify them. Management Science, 64(3), 1155–1170.
19. Caraban, A., Karapanos, M., Gonçalves, D., & Campos, P. (2019). 23 ways to nudge: A review of technology-mediated nudging in human-computer interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–15).
20. Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A flaw in human judgment. Little, Brown Spark.
21. Kiseleva, A., Williams, D. M., & Chowdhury, G. G. (2018). Intelligent personal assistants: A literature review. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (pp. 305–308).
22. Duque, R., Fred, A. L. N., & Gamboa, H. (2018). Intelligent personal assistants: A systematic literature review. Expert Systems with Applications, 114, 10–29.
23. Lopatovska, I., & Williams, H. (2018). Talk to me: Exploring user interactions with the Amazon Alexa intelligent personal assistant. Journal of Librarianship and Information Science, 50(4), 392–407.
24. Pradhan, A., Findlater, K. R., & Lazar, A. K. (2019). ‘Phantom friend’ or ‘just a box’? Towards a deeper understanding of intelligent personal assistant use by older adults. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–14).
25. Bescós, P., Følstad, A., & Sanagustín, P. P. (2021). Designing voice assistants for critical thinking support. In A. Følstad et al. (Eds.), Conversational user interfaces (pp. 135–150). Springer.
26. Cheng, Y., He, J., & Wallace, V. L. (2019). Combating confirmation bias in online search with a conversational agent. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–12).
27. Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human–AI interaction (HAII). Journal of Computer-Mediated Communication, 25(1), 74–88.
28. Straub, D. W., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management, 33(1), 1–11.
29. Zakour, A. B. (2004). Cultural differences and information technology acceptance. In Proceedings of the Annual Meeting of the Decision Sciences Institute.
30. Tarhini, A., Hone, K., & Liu, X. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of e-learning in developing countries: A structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328.
31. Nisbett, R. E., Peng, K., Choi, I., & Norenzayan, A. (2001). Culture and systems of thought: Holistic versus analytic cognition. Psychological Review, 108(2), 291–310.
32. Yang, Q., Ellison, N. B., & Heeter, C. L. (2019). Understanding the effects of culture and social self-efficacy on social media use: A comparative study of American and Chinese college students. Computers in Human Behavior, 97, 173–182.
33. Chien, Y.-H., Lin, C.-L., & Chen, Y.-C. (2018). Cultural influences on users’ trust in recommendation agents. Computers in Human Behavior, 87, 1–10.
34. Bankole, F. O., & Onifade, O. F. (2023). Intelligent personal assistants research: A systematic literature review and future research agenda. International Journal of Information Management Data Insights, 3(1), 100153.
35. Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage Publications.
36. Johnson, M., Smith, L., & Davis, R. (2022). Explainable AI and cognitive bias mitigation in North American users. Journal of AI Ethics, 2(3), 215–230.
37. Zhang, W., Lee, S., & Kim, J. (2021). Personalized nudges and anchoring bias in East Asian e-commerce platforms. International Journal of Human-Computer Interaction, 37(15), 1420–1431.
38. Müller, H., Dubois, C., & Rossi, S. (2020). Statistical framing reduces availability heuristic in European financial decision-making. Journal of Behavioral Finance, 21(4), 380–392.
39. McSweeney, B. (2002). Hofstede’s model of national cultural differences and their consequences: A triumph of faith—A failure of analysis. Human Relations, 55(1), 89–118.
40. Baskerville, R. F. (2003). Hofstede never studied culture. Accounting, Organizations and Society, 28(1), 1–14.
41. Oppong, N. Y. (2013). Guide from Hofstede’s cultural dimensions. Cross-Cultural Management Journal, 15(1), 67–72.
42. Woldu, H. G. (2001). Looking at African value system through cultural dimensions. International Conference on African Development Archives, Paper 25.